scikit-learn video #5: Choosing a machine learning model

Welcome back to my video series on machine learning in Python with scikit-learn. In the previous video, we learned how to train three different models and make predictions using those models. However, we still need a way to choose the "best" model, meaning the one that is most likely to make correct predictions when faced with new data. That's the focus of this week's video.

Video #5: Comparing machine learning models

How do I choose which model to use for my supervised learning task?

How do I choose the best tuning parameters for that model?

How do I estimate the likely performance of my model on out-of-sample data?

We'll walk through two different procedures for evaluating our models. The first procedure allows us to calculate the training accuracy, which is a measure of how well our model classifies the training observations. Unfortunately, choosing a model based on training accuracy can lead to overfitting:

An overfit model, like the one pictured above, has learned the noise in the data (the green line) rather than the signal (the black line). To avoid overfitting, we'll use a different evaluation procedure that splits our existing data into training and testing sets:

That allows us to calculate the testing accuracy, which better estimates the likely performance of our model on future data. As well, we can locate the optimal tuning parameters for our model by examining its testing accuracy at different levels of model complexity.

If you want to understand this week's material at a deeper level, I strongly recommend that you review the two resources below on the bias-variance tradeoff. It's a critical topic that shows up throughout machine learning, and will help you to gain an intuitive sense for why models behave the way they do.

Next time

In the next video, we'll learn our first technique for modeling regression problems, in which the goal is to predict a continuous response value. We'll also learn how to read a dataset into Pandas, a very popular library for data cleaning and analysis, so that it can be transformed for use with scikit-learn.

As always, I appreciate you joining me and would love to hear your comments and questions below! Please subscribe on YouTube to be notified of the next video, and I'll see you again in two weeks.

Need to get caught up?

The lower is the number of neighbors used (k), the more complex the classification boundaries will be, and it's easy to see why. Suppose the model is trained with 50 samples. If k=1, There are 50 possible regions that a new point can be assigned, however, if k=50, there is only 1 possible region (the region formed using every point). Sure, it would be a useless model, which always predicts the same class, but it's just to show that lower k leads to more complex models.

Eugene Kudko

It's a really great set of introductory level tutorials. I know most of these things, but still decided to watch every video in order to structure my knowledge. Very and very useful. Plus extremely easy to follow. Thank you!